Many large organizations use enterprise resource planning (ERP) systems to consolidate day-to-day transaction data and streamline business functions such as manufacturing. With their predefined, standard reporting capabilities, however, these ERP systems are not optimized to support the flexible, ad hoc business analysis and reporting businesses need to make strategic decisions and improve business performance. Furthermore, ERP systems are not intended to serve as e-business analysis and reporting infrastructures.
For example, generating a report from an ERP system that shows product line sales by region by sales person for the past five years would typically be quite time-consuming. With their multitude of tables, fields, and column names, ERP systems are not well suited to end-user navigation. Without easy information access, and the means to quickly analyze and report on findings, users can overlook important business correlations or veer off-track completely. Ultimately, the quality and speed of decision-making suffer.
In addition, if hundreds or thousands of users were to submit queries directly, ERP system performance would be impacted, jeopardizing important production system functions. This, along with the risks associated with giving the extended e-business enterprise direct access to ERP systems, necessitates placing ERP data into an environment that is not only optimized for business analysis and reporting, but also for secure broad access. Seeking predictable performance and desiring to give users all the information they need quickly, many companies opt to build either data warehouses or data marts.
Companies which have strived to develop decision support systems that would support rich analysis and reporting realized that operational reporting systems (e.g., ERP systems) were limited in scope and the depth of insight they delivered. While optimized for consolidating day-to-day transaction data and streamlining key business functions, these systems offer but a fraction of the reporting and analysis capabilities users need to fully comprehend what drives business performance.
Many companies turned to developing data warehouses to fill the requirement for consolidating data from across the organization, with a single consistent historical view, and designed for optimized reporting and analysis. The ultimate objective of these systems was to ensure that the data needed to answer the relevant business questions was captured and in a form that would support timely information for decision-making While the intent was sound, the challenges of bringing together business and IT to define best practices from both a business and technical standpoint presented challenges. As a result projects failed resulting in decision makers being left without crucial information.
Created by extracting data from operational or transactional systems (like ERP sources) and e-commerce systems and installing it in a more analysis- and reporting-friendly database, data warehouses are repositories of data that support management decision-making. However, data warehouses are expensive to build and time consuming. (For example, they can take 18 to 24 months to create). Consequently, with enterprise information requirements evolving so fast today, data warehouses often fail to meet requirements when they are finally completed. Moreover, they require specialized skills and experience to build successfully.
Furthermore, due to their sheer scope, data warehouses seldom produce the finely tuned analysis and reporting that e-business decision-making depends upon. Intended to be all things to all people, these warehouses focus on breadth of content, rather than the depth of vital information sweet spots users need.
Unlike data warehouses that combine and make all corporate data available across an enterprise, data marts focus more narrowly, serving specific business areas or departments. Data marts also take less time and money to build and can therefore generate quicker payback than data warehouses.
Sound in principle, data mart creation can stumble in practice. While data marts can be built incrementally, they do not provide a holistic view of the enterprise. Companies will build a data mart for sales, another for inventory, another for finance, and so on. Unless these marts are coordinated, they act as stovepipes and prevent users from sharing information across the enterprise. They also duplicate data and lead to lengthy updates because each mart must be refreshed individually. If companies update the marts at different times, even just a couple of hours apart, some users will have more current information than others. This lack of synchronization can lead to inconsistent analysis across the enterprise and cause users to question the integrity of the analysis and reporting solution.
For instance, users of one mart might define a “large” customer as one that generates more than $50,000 in revenue a month. Users of another might define a large customer as one that orders more than 100 units a month, which may only represent $10,000. In these cases, people can mistakenly think that they are discussing common ground. Not only may different marts define dimensions differently, they can calculate measures differently as well. For example, one department might compute “profit” by including bad debts and another may exclude them. These types of inconsistencies not only create misunderstandings, they can delay schedules and increase costs, jeopardizing customer satisfaction and profits.
There is a need for affordable data warehouse technology, which an enterprise can use to achieve and maintain a complete view of its operational and financial effectiveness, customer relationships, and supply-side activities.